The effect of a stock-based M&A deal on the acquiring
firm’s value during hot markets
Author: Mart Boers Student ID: S4801105
Specialization: Corporate Finance & Control Supervisor: Dr. D.J. Janssen
Date: August 14th, 2020
Abstract
This study examines the effect of stock-based M&A deals during a hot market period on the performance of the acquiring firm, compared to stock-based M&A deals that did not occur in a hot market period. The performance of the acquiring firm is measured with the cumulative abnormal returns. To determine whether a hot market is apparent, the method of Tebourbi (2012) is used. A hot market is operationalized by retrieving the highest quartile of the used stock market index, and it must hold it for at least three consecutive months. The sample of the study is based upon European firms, since little research has been done with respect to this research problem for European firms. Prior research, based upon US data, showed that in the short run, deals made in a hot market period outperform the deals made in other periods, but mixed results have been concluded for the long run. The results of this study are in line with the US-based studies, with respect to the short run. However, there is not enough evidence to provide an answer for a long-term effect on this research problem in this study.
Key words: Mergers & Acquisitions, market timing differences, European perspective, abnormal returns.
Table of contents
1. Introduction 4 2. Literature review 7 3. Research method 12 3.1 Data sample 12 3.2 Dependable variable 13 3.3 Independent variable 15 3.4 Control variables 15 3.5 Models 16 4. Results 18 4.1 Analysing hypothesis 1 18 4.2 Analysing hypothesis 2 20 4.3 Robustness check 214.4 Comparing results with prior studies 23
5. Conclusion 24
6. Bibliography 26
1. Introduction
In the financial world as of today, Mergers and acquisitions (hereafter M&A) play a big role in the world of corporate finance. Since the year 2000, 790,000 M&A deals have been confirmed, with a total value of at least 57 trillion US Dollars (Imaa-Institute, 2020). Due to the large scale of M&A deals, it is no surprise that a lot of research has been done on M&A and of its long-term profitability. Studies have shown that the magnitude of M&A deals come in clusters, or so-called merger waves.
One of the factors influencing the occurrence of a merger wave is the overvaluation of the shareholder value of a firm. Since, a firm is overvalued, it wants to increase its shareholder value by buying less overvalued firms with their own stocks. Literature has shown that this occurs in the real world (Rhodes-Kropf, Robinson and Viswanathan, 2005; Dong et al., 2006; song 2007), however the effects on the performance of mergers due to overvaluation of firms are mixed. The neoclassical schoolargues that shareholder value will increase, when applying the method, because synergies will be created, which improves the performance of the acquiring firm and therefore also its shareholder value. The bidding firm will swap its relatively overvalued shares for relatively cheaper ones from the target. The firm will therefore get a boost in shareholder value. This corporate strategy is explained by Schleifer and Vishny (2003).
However, more recent literature (Akbulut, 2012; Song, 2007; Fu, Lin & Officer, 2013) state that it reduces shareholder value, because of the overpayment for the target and or managerial compensations. They argue that bidders fail to examine the true value of the target due to the hot market. They, often, overstate the added synergies, and therefore they will pay a higher premium for the acquisition. The synergies will not outweigh the high premium, causing a loss in shareholder value and the overall performance of the bidding firm. This does not necessarily take place in the long run only. The first signs are already visible a few days after the deal completion.
The literature on this corporate finance phenomenon is thus mixed. Furthermore, most of the research has been based on US-firms and therefore on US-based M&A deals. Taken the empirical and hypothetical research previously discussed into account, a research question can be formulated. The research question will be as followed:
To what extent do European stock-based M&A deals performances during hot markets differ from the stock-based M&A deals that occurred in other market periods?
Research done on European M&A deals with respect to market timing differences has scarcely been performed. One reason for this might be, because the stock markets for US-firms are more important than for European firms, since European firms rely more on bank finance
(Chakraborty & Ray, 2006). Nevertheless, it is still intriguing to conduct research on this topic from a European perspective, for example for practical and theoretical reasons. If this study examines different effects compared to the US conducted studies, companies might alter their corporate strategy with regards to M&A. If stock-based M&A deals during a hot market do not perform worse than the market index during that time or to other time periods, companies might perform more M&A deals, due to the reasoning of Schleifer and Vishny (2003) that the bidding companies can swap their overvalued shares with cheaper ones, as mentioned above. Also, this research is relevant at this moment in time1, since stock markets are continuously rising over the last few years, making at highly possible that there are publicly traded firms that are overvalued, and therefore creating the
possibility to pay with their expensive stocks in order to buy another firm.
Motivated by the mixed results of earlier literature, the lack of empirical evidence for European firms and the practical relevance, since stock markets in 2020 are at a sky-high (Factset, 2020), this paper will focus on the effects on performance from stock-based M&A deals during hot markets compared to other times, more specifically the difference between hot markets and cold and normal markets2 and to the market index3. This paper will give a more recent look at the problem, and as mentioned earlier, it will look at European firms, rather than US firms, which might give different insights on this topic. The reason why this has theoretical relevance, is because it may give different insights since Europe is less dependent on stock market prices, compared to the US. According to the literature, US firms rely more heavily on stock market changes, so more companies in the US need to anticipate more on stock market changes compared to European countries (Chakraborty, & Ray, 2006). So first, it might be that the overall number of stock-based M&A deals during a hot market in Europe is lower, compared the US. Furthermore, since European firms are less dependent on the stock market, it might be, that they simply do not engage in a M&A deal for the sole purpose to swap their overvalued shares for less overvalued shares. It can then be assumed that stock-based M&A deals in Europe might contain more fundamental reasoning than the theory of Schleifer and Vishny (2003) that imply for US-based firms. Therefore, it is important to test this phenomenon on European firms, to examine whether European firms do indeed behave differently, or whether they follow the theoretical framework of Schleifer and Vishny (2003) and gain a lot of value in the short-run during times of overvaluation.
The results of this study also contain social relevance. Regardless of what the outcome of the effects will be, European firms can extract useful insights of this paper for their own business. If
1 This time referring to the year 2020.
2 Cold and normal markets are treated as one, since the focus of this study is to study the difference from a hot market compared to other times, regardless if this is a normal or cold market.
overvalued firms have doubts about their corporate strategy with regards to their overvaluation, they might pursue a M&A deal, or for example, buy up their debts.
This study will continue with an overview of the relevant literature on this research topic. Next, in chapter 3, the research method will be explained and what methodological approach will be used to test for it. After that, in chapter 4, the results will be shown, analysed, and compared to prior research. In chapter 5, the conclusion of this paper will be given. Chapter 6 will contain the
bibliography of this study and at last in chapter 7 the appendices are shown, where all conducted test in this study will appear.
2. Literature review
First, it is important to note that mergers tend to come in waves (Town, 1992)4. According to Town (1992), merger waves are a non-linear phenomenon and that merger waves are endogenous. This means that when the industry is triggered by something, the mergers will keep following each other, because firms do not want to be left behind. It is comparable with a prisoner’s dilemma (Yan, 2011). The triggers are often based upon shocks, caused by for instance a tax reform or any other
macroeconomic factor. However, these triggers also create a high market optimism, which lead to higher stock prices, and therefore will trigger more mergers to happen (Gugler et al., 2012)5.
So, when this shock occurs, stock markets rise causing a potential overvaluation of a firm’s shareholder value. In principle, the corporate strategy of overvalued firms can go in three directions (Gugler, Mueller & Yurtoglu, 2006): (1) Acquire another firm, (2) pay off the debts or (3) do nothing. The question is whether a M&A during such a merger wave is beneficial for the acquirer.
Schleifer and Vishny (2003) made a hypothetical model that suggested that acquirers that pay with stocks are incentivized due to their relative overvaluation compared to the target. Schleifer and Vishny (2003) thus suggest that an overvalued company benefits from their overvaluation, when they can swap their stocks for cheaper ones with help of an acquisition. They proved their point by pointing out that the merger waves of the 1960’s and 1990’s were during a time of high stock markets and that in those merger waves most merger payments were accomplished with stock, whereas in the 1980’s (a time of relatively low stock market prices) mergers were majorly financed with cash. Therefore, overvalued companies do want to merge with a relatively less overvalued company, to gain from their overvaluation. This theoretical framework of Schleifer and Vischny (2003) is supported by other studies that examined why acquirers are incentivized to pay with stock at certain times (Lambrecht, 2004; Morellec & Zhdanov, 2005)
Gugler et al. (2012) give an additional reason as to why most mergers are paid with stocks during merger waves rather than with cash. According to Gugler et al. (2012), borrowing costs of debt are relatively high during merger waves, because the entire economy is booming. This also causes interest rates to rise, therefore it is more beneficial for the acquirer to pay with stocks, rather than with cash.
Rhodes-Kropf and Viswanathan (2004) also made a hypothetical model that, just like Schleifer and Vishny (2003), argue that overvaluation of a firm lead to more mergers. Rhodes-Kropf and Viswanathan (2004) also notice the high stock payments during such times, which is in line with the hypothetical model of Schleifer and Vishny (2003). The model of Rhodes-Kropf and Viswanathan
4 Town (1992), performed his study based on 4 US datasets and 1 UK dataset ranging from 1919 to 1954.
(2004) explains that a target firm underestimates the market wide effect and therefore is not able to make a good judgement whether the acquiring firm is overvalued. This leads to the fact that during times of overvaluation of firms, many M&A deals will succeed, because target firms are not able to make good judgements during stock market booms. They will overestimate the synergies, whereas in reality the acquiring firm is highly overvalued, and the synergies are not as high as expected. In both hypothetical models, it is important to note, that perfect markets are not assumed. If markets are perfect, firms will not be overvalued and thus the whole theory will not make sense anymore. Therefore, they use imperfect market in their models, because in imperfect markets, there is room for mispricing of assets and stock prices.
To prove the hypothetical models of Schleifer and Vishny (2003) and Rhodes-Kropf and Viswanathan (2004), Rhodes-Kropf, Robinson and Viswanathan (2005)6 made an empirical model in order to test whether overvaluation of firms leads to an increasing number of mergers. In their model, the market-to-book ratio (hereafter M/B ratio) is their key statistic. According to Rhodes-Kropf, Robinson and Viswanathan (2005) (Hereafter, RKRV), the market-to-book ratio is the ratio to determine whether a firm is overvalued or not and to what extent it is overvalued. The results of RKRV (2005) are less straightforward than expected. The acquirers do have the largest M/B ratio; however, the target of the acquirer has a higher M/B ratio than companies that do not engage in merger activity. So, the highest M/B firms that do acquire lower M/B firms, but these lower M/B firms, on average, have a higher M/B ratio than the average firm. Furthermore, RKRV (2005)
recognize that cash targets are less undervalued than stock targets and that stock acquirers are more overvalued than cash acquirers. This is in line with both hypothetical models discussed earlier, because overvalued firms want to swap their rather overvalued stocks for more cheaper, less overvalued stocks in a form of a merger. The reverse effect is true for cash deals. Also, RKRV (2005) conclude that merger activity is highly correlated with short-term valuation deviations in a long-term trend. This effect is even higher when stock payments are used. This empirical evidence is in line with the hypothetical models.
Dong et al. (2006)7 conducted a similar research as RKRV (2005) did. However, their proxy for misvaluation is book value / price and residual income / price. The results of Dong et al. (2006) are in line with RKRV (2005), Schleifer and Vishny (2003) and Rhodes-Kropf and Viswanathan (2004). Song (2007)8 also examines a correlation between merger activity and mispricing of firms, and therefore question the neoclassical concepts of perfect markets, because in perfect markets such events will not be able to occur, due to perfect pricing of the value of a firm.
6 Rhodes-Kropf, Robinson and Viswanathan (2005) used a US-based sample from the period 1978 to 2001.
7 Dong et al. (2006) used a US-based dataset starting at 1978 and ending at 2000. 8 Song (2007) used a US-dataset with a time period ranging form 1986 to 2000.
RKRV (2005) and Song (2007) have empirical evidence that merger activity is influenced by mispriced firms. However, little has been said about the success rate of these mergers. Fu, Lin and Officer (2013)9 extended the work of RKRV (2005) and Song (2007). Using the research method of RKRV (2005), Fu, Lin and Officer (2013) examine whether the stock financed mergers during times of a hot market turn out to be successful, like the hypothetical models of Schleifer and Vishny (2003) and Rhodes-Kropf and Viswanathan (2004) suggest, or if they most likely will fail. Fu, Lin and Officer (2013) discover that stock mergers during overvaluation perform good in the short term, but poorly in the future. The premiums paid are too high, because during times of overvaluation it is hard for the acquirer to identify the fundamental value of the target. The synergies are minimal, therefore the premium that has been paid outweighs the benefits of the synergies, causing the M&A deal to fail. Furthermore, Fu, Lin and Officer (2013), state that the merger deals are not done for the shareholder value like the hypothetical models of Schleifer and Vishny (2003) and Rhodes-Kropf and Viswanathan (2004) state, but for managerial compensation. Fu, Lin and Officer (2013) state that during times of overvaluation acquirers’ benefit from mergers for personal interest and not for the firm itself. Savor and Lu (2009)10 also concluded that overvalued firms engage in stock-based M&A deals, and that these deals turn out to be profitable for their shareholders. However, Savor and Lu (2009) point out that the increase in firm value is solely artificial, therefore stating that in the long run, this might hurt these companies. The M&A deals are solely executed for the purpose to generate extra income for the managers, suggesting that such merger deals are not permanently beneficial. This finding is in line with Jensen (2004). Jensen (2004) stated that CEO’s could engage in M&A deals when stock prices are very high, to maintain these high stock prices, even though it would harm the firm in the long run. Fung, Jo and Tsai (2009)11 conducted similar research as Jensen (2004) and reached the same conclusions. They argue that the theoretical framework still holds and that in the short run stock-based M&A deals in overvalued times are beneficial.
Song (2007) also state that mergers during times of hot markets tend to fail more often than mergers that take place during ‘normal’ financial times for the long run. Song (2007), however, looks at the perspective of insider trading, and sees that acquiring managers that tend to sell their shares prior to the merge are doomed to fail. Song (2007) finds that such a pure sellers’ perspective is detrimental for the firm in the years after the merge, but in the short run the acquiring firm’s
shareholder value will increase. So, Song (2007), also acknowledges that stock paid M&A deals during hot markets are more often susceptible for poor performances than non-stock deals or stock deals that do not take place during hot markets. Akbulut (2012)12 also concludes the same as Song (2007).
9 Fu, Lin and Officer (2013) used a US dataset starting at 1985 and ending at 2006. 10 Savor and Lu (2009) used a US dataset from 1978 to 2003.
11 Fung, Jo and Tsai (2009) used a US dataset ranging from 1992 to 2005. 12 Akbulut (2012) used a US dataset starting at 1993 to 2009.
Therefore, again, acknowledging that on the short term13, the deal performance increased more than in cold or normal markets and in the long run the reverse effect will happen. The question therefore arises if the neoclassical view on M&A still holds. Synergies are often overstated as mentioned above, and managers might profit from M&A, even though it destroys the performance of the firm in the long run.
The existing literature has mixed results with regards to merger success. The neoclassical view is that increased merger activities are a result of shocks, and that such mergers will create synergies that will improve the acquirer’s performance, especially in the long run and during the time of the merge. However empirical evidence has shown that this performance during a hot market
period/times of overvaluation will be worse, especially when stock is used for financing the M&A deal. Thus, there are conflicting views with regards to the success rate of stock-based M&A deals during hot market periods. This research can be of major relevance, to tell which theories are
supported in this research. Furthermore, much of the literature is based on US-firms. This could cause a bias in the literature. Therefore, this paper will focus on European firms instead. The results might differ from similar conducted empirical analyses, that was based on US-firms. Therefore, this research is of relevance, because it takes European companies into account, which could change the view on this research area.
In order to answer the research question the following hypotheses have been made:
H1: In the short-term, stock-based M&A deals during hot markets in Europe perform better than stock-based M&A deals during normal or cold markets
H2: In the long run, stock-based M&A deals during hot markets in Europe do not perform better or worse than stock-based M&A deals during normal or cold markets
According to the literature discussed above, the most recent studies have similar outcomes with regards to H1. Although all this research has been conducted in the US with US firms, it is still expected that the same applies for European companies, since there is, yet, no studies that contradict with this evidence for European countries. H2 is based upon the fact that results of prior research are rather mixed. Older literature stated that M&A deals were always beneficial, also in the long run, but more recent literature shows a reverse effect. Since these conclusions are contradicting, this study will lay in between both sides. Therefore, this study expects that stock-based M&A deals are neither better nor worse in the long run, compared to other market periods.
3. Research method
In this chapter the methodology of this study will be explained. Section 3.1 discusses how the sample was formed, the sample itself and its criteria to be part of the sample. Sections 3.2 will focus and the dependent variable of the study. Thereafter, section 3.3 will specify the independent variable used in this study. Section 3.4 will elaborate on the control variables and at last, section 3.5 will explain the models used, to get the appropriate results for this study.
3.1 Data sample
To answer the research question of this study, a quantitative research method is used. All the data regarding mergers and acquisitions, stock data of all the companies and of the index (EuroStoxx 50) will be retrieved from Factset. Factset uses a wide range of sources and contains a wide range of M&A deals. This makes Factset well suited for this study. Factset does not only provide information about the deal itself, but also its size, its industry, deal-specific information, and the form of payment. The form of payment is crucial in this study since it focuses solely on stock-based M&A deals. Due to this feature, this study can identify if stock-based M&A deals during hot markets did indeed meet its expectations explained earlier.
The sample used in this study will only include European companies, both acquirer and target, starting at January 1st of 2000 and ending at December 31st of 2015 who engaged in a solely stock-based M&A deal that has been completed. The reason that the sample starts in 2000, and not for instance in 2010, makes this study more time-relevant, because prior to 2010 hot markets were more occurrent than after 2010 (Gugler et al., 2012). Also, there is a big discrepancy between the amount of stock-based deals between the United States of America and Europe. The number of deals completed in Europe is rather small (only a few 100), whereas American completed deals exceed over 10,000 (Factset, 2020). The data sample only consist of European firms, both acquirer and target that have been listed. Since this study examines the effects of stock-based M&A deals, the sample must consist of companies that are publicly listed. The sample does not have a restriction regarding excluding or including certain stock markets. If the company is publicly listed, it will be sufficient for this study, because excluding or including certain European stock markets will not influence the outcome of this study.
The only exception to this is financial companies. Financial companies, such as major banks and investment banks have been excluded from the sample, although they made up the largest portion of the sample. The reason it has been excluded is to prevent bias in the sample. The financial industry has not the same regulatory framework as other industries, making stock-based M&A deals
easier and their reasons to execute such deals are too different from the other industries (Vafeas & Theodorou, 1998). So, even though the sample gets reduced heavily, it still would benefit to exclude this industry to prevent bias in the sample. The only type of M&A deal that has been included in the sample is a completed deal. Rumours, unfinished deals and self-tender offers have been excluded, since they do not have a purpose in this study, because this study wants to examine the effect of a change in firm value due to an actual stock-based M&A deal. Furthermore, self-tender offers may inflict bias in the sample, due to the fact that it is seen as defence mechanism against a takeover (Lie, 2002). The sample also consists of a few companies that have completed multiple stock-based M&A deals during the timespan of the sample. Only the first M&A deal of these companies have entered the sample, to avoid bias. At last, there have been documented stock-based M&A deals of companies that do not exist anymore or have been privatized. The returns of these companies were not
available, so these companies also have been excluded from the data sample.
3.2 Dependent variable
To be able to test whether stock-based M&A deals during hot markets perform better in the short run, a dependent variable must be chosen to examine this effect. To examine the performance of the deals, the study measures the cumulative abnormal returns of the stocks of the acquirers. The reason this study chooses cumulative abnormal returns (hereafter CAR) over other measurements is due to its wide acceptance in the academic literature and the CAR of a company cannot be altered by the acquiring companies, since the CAR is based upon stock prices that are publicly available at all times. Furthermore, the CAR is ought to be serially independent (Dodd and Warner, 1983; Cowan, 1993) These traits of the CAR increases the credibility of the method, since it is certain that the company cannot interfere with the firm’s performance. Whereas manipulation with the CAR is not possible, other measurements to measure the performance of the merger deal, like for instance returns based on accounting principles, can be altered (Shoven, 1975; Burgstahler & Dichev, 1997). Using
measurements like these would cause a decrease in the reliability of the outcome of this study. To calculate the CARs, the abnormal return (AR) and the market return, in this case EuroStoxx 50) must be calculated prior and after the announcement date of the merger deal. The abnormal returns of the companies are calculated with use of an estimation window. This study uses an estimation window of -100 to -15 days prior to the announcement date. The estimation window does not end at zero, because the purpose of the estimation window is to not be affected by the merger deal. The reason why this study starts the estimation window at -100 and not for instance -200, is to ensure that no other factors play a role in the abnormal returns, which would cause bias. Therefore,
the estimation window is rather short, but not too short to not be able to make a proper abnormal returns estimate. The given formula to calculate the AR is used by Ikram and Nugroho (2014).
R
it=
α
i+
β
iR
mt+
ε
it(
1)
R
it=
¿
Rate of return on the security price of company i on day t.αi=¿ An estimated parameter during the estimation window which shows the average return on
security i when there the market return is absent.
βi=¿ An estimated return during the estimation window which shows the systematic risk of stock i
Rmt=¿ rate of return of the market index (EuroStoxx 50) on day t.
ε
it=
¿
The error term, which in this study is 0.After the abnormal returns of the estimation window have been calculated, the abnormal of the event window will be calculated. In the event window, the timing of the M&A announcement takes place. So, the time interval of the event window captures the M&A announcement. The length of the chosen interval is important, since the effect changes rapidly when lengthening the window (McWilliams & Siegel, 1997). When using a shorter window, it will it is not certain that most M&A announcement effects will be captured in the model. This is due to inefficient markets that fail to adopt to correct prices quickly (Amewu, 2014). However, when using a wider window, there is the risk that other events, other than the M&A deal will captured, causing a decrease in the significance of the M&A deal (Manzoor, 2015). Due to the fact that companies in Europe are located on different stock indices, where some are considered more efficient than other, this study will use an event window that does not reflect a perfectly efficient market that suffers from little information leakage, nor a very undeveloped market that does suffer to reflect the price rather quickly. The event window will therefore be set at -1 to 1 days14, which is in line with (Tebourbi, 2012; De Bodt, Cousin & Roll, 2015).
After calculating the abnormal returns, the cumulative abnormal returns can be derived. Using the CAR prevents that investors might get different returns per day, because the CAR is the sum of the AR. Therefore, the CAR is a better predictor of the acquirers’ performance than the AR (Brown & Warner, 1985).
CA R
i ,t1, t2=
∑
t=t1 t2
A R
it(2)
CA R
i ,t1, t2=
¿
Cumulative abnormal returns for each company i over periodt
1 tot
2 .t
1=
¿
Start of event window at -1 days before the announcement date of the M&A deal.t2=¿ End of event window at +1 days after the announcement date of the M&A deal
When the CAR of a company turns out to be above zero, investors updated their prior believes and expect higher future firm value due to the M&A deal.
3.3 Independent variables
The independent variable in this study will be a dummy variable to examine whether there is a hot market or not. This is a method used before by (Tebourbi, 2012) to tell whether firms are overvalued or not. Tebourbi (2012) determined that, to have a hot market, the stock index15 must be in the highest quantile of all returns from the sample for three consecutive months. This means that a hot market will always have a duration of at least a quarter of a year. Other papers that emphasized on the effect of stock-based M&A deals looked at financial statements, most often the market-to-book ratio (Schleifer & Vishny, 2003; Rhodes-Kropf, Robinson & Viswanathan, 2004; Rhodes-Kropf, Robinson & Viswanathan, 2005) . However, in practice, it is hard to determine how much a firm is overvalued by looking at the market-to-book ratio alone. Furthermore, the techniques (Schleifer & Vishny, 2003; Rhodes-Kropf, Robinson & Viswanathan, 2004; Rhodes-Kropf, Robinson & Viswanathan, 2005) to capture the market-to-book ratio as the independent variable for their study is beyond the scope of this study. Therefore, this study will use the technique of Tebourbi (2012) to make a dummy variable that tells whether the market is hot or not.
3.4 Control variables
Prior studies in this research domain have been rather limited with concern to control variables. These studies have not been overloaded with control variables but were kept rather simple. This research will be no different with regards towards the control variables. Many studies in this field, like Song (2007) already controlled for the industry beforehand, by removing the financial industry. As explained before, the financial industry does trouble the reliability of stock-based M&A deals studies.
15 The stock index in this study will be EuroStoxx 50 since it is a European dataset. To avoid bias by picking a national index, a European index has been chosen.
In line with Song (2007), this research opted for the decision to remove the financial industry beforehand and to control for the remaining industries afterwards.
Also, this study will not have a control variable for time. The reason for this is that it is already controlled for in the independent variable16, because the independent variable in this study looks in a sense for market sentiment. Time controls are used to eradicate any specific market sentiments; however it is exactly the goal of this study to see whether M&A deals during times of market
sentiments differ in performance from ‘normal’ market times. Therefore, there will be no controlling done for time. This is in line with the reasoning of (Tebourbi, 2012).
Also, studies regarding M&A deal performances (Rhodes-Kropf, Robinson and Viswanathan, 2005; Dong et al., 2006; song 2007) controlled for payment type. Since this study solely looks at the effect of stock-based M&A deals, this control variable is of no relevance for this study and will therefore not be included.
This study will however control for the size in two ways. The first controlling variable
regarding size will control for the market value of the acquiring firms17. Like, Song (2007) this firm size will be in a form of a natural logarithm as it would otherwise fail the kurtosis and skewness test. The second size related variable will control for the size of the deal payment18. Since there are hot and cold markets in the sample, differences in payments will be expected, because in hot market as stated earlier in chapter 2, premium paid will be higher. This is an economic phenomenon that this study wants to control for.
At last, this study will control for a hostile takeover. Some studies, previously discussed, (Akbulut, 2012; Dong et al., 2006; Fu et al., 2013) measured the attitude of the deal to capture any possible overpayments. Hostile takeovers usually, as stated earlier, lead to overpayments, therefore controlling for this scenario, lead to a lower chance of bias and chance of an omitted variable in the sample. Therefore, a dummy variable will be created in this study to capture the effect for a hostile takeover. This dummy variable will be called ‘attitude’.
3.5 Models
To test the effect of the hot market dummy on the cumulative abnormal returns (hereafter CAR or CARs), an ordinary least squares regression (hereafter regression) will be performed. The OLS-regression is useful for this study because it helps to explain the relationship between stock-based merger deals during a hot market period and the cumulative abnormal returns. Since this is the sole purpose of this study, other regressions do not increase the relevance of this study. Therefore, an
16 The hotmarket dummy variable, named hotmarketdummy. 17 This control variable will be called FirmSize.
OLS-regression will be used for this study. To fulfil an OLS-regression it must pass certain tests to make a valuable judgement of the given results by the OLS-regression. The first tests are being taken to detect possible outliers in the sample. The most suited tests are DfFit and Cook’s Distance19 (Berry & Feldman, 1985; Kianifard & Swallow, 1989). Then, in Stata checks will be conducted whether the variables suffer from skewness and kurtosis. The control variables FirmSize and DealSize failed the test and therefore the natural logarithm have been taken20, to control for the issue and to ensure that the regression will not be biased because of it. Also, a vif-test will be conducted to spot any
multicollinearity issues in the sample. Once everything is tested and corrected for the possible issues discussed in this paragraph, the OLS-regressions will take place.
19 A test that detects data points in the sample that have a considerate influence on the whole dataset and thus the regression and therefore will be removed.
4. Results
4.1 Analysing hypothesis 1
First, hypothesis 1 is tested. This is done by setting the event window at a narrow time frame. As already mentioned in chapter 3, this window is -1 to 1. The hypothesis states that in the short run, the companies that performed a stock-based M&A deal during time of a hot market, will perform better than normal and cold markets. The results in table 1 support the hypothesis. The variable that controls whether the market is hot or not is the variable called ‘hotmarketdummy’. The
hotmarketdummy variable is positive with a coefficient of 7.579. This means that the companies that
executed their stock-based M&A deal during the hot market periods scored 7.579 percent higher than other periods. Also, even though the sample size is low due to a lack of available data, the regression is significant at five percent significance level. This means that the hotmarketdummy is significant at a five percent significance level. The R-squared has a value of 0.575. This means that the variables in the regression explain 57.5 percent of the regression. However, a possible explanation for the low adjusted R-squared is the number of variables. Simply adding more independent variables tends to increase the adjusted R-squared. This has not been done in this study, due to previously given reasoning in chapter 3. The control variables regarding the deal size and the firm size of the selected companies have a negative influence on the CAR (-0.159 and -0.133 respectively), however these coefficients are not trustworthy since both variables are insignificant. The attitude coefficient is positive with a size of 2.356, meaning that a hostile takeover as positive effect on the CAR. However, once again, this coefficient is not meaningful for this analysis since it has no significance. This means that when selecting other companies for a different study, the value of the coefficient can easily differ from the coefficient in this study. At last, all industry coefficients are insignificant, and therefore, no relationship with the CAR can be found. However, the insignificance of the control variables is not of any concern for this study since the study solely examines the effect of the hotmarketdummy on the CAR. In short, the fact that the hotmarketdummy variable is significant at a five percent level is most important.
Table 1: OLS-regression of the full sample at an event window of -1 to 1
21.
(1)
VARIABLES
OLS
hotmarketdummy
7.579**
(3.229)
ln_DealSize
-0.159
(0.356)
ln_FirmSize
-0.133
(0.447)
attitude
2.356
(3.543)
i1
3.484
(7.277)
i2
5.208
(9.276)
i3
6.885
(8.391)
i4
5.532
(9.254)
i5
7.584
(9.246)
i6
-3.519
(9.457)
i7
3.958
(8.149)
i8
0.931
(7.991)
i9
7.000
(9.332)
i10
6.699
(8.207)
Constant
-3.855
(8.515)
Observations
99
R-squared
0.575
21 The first 10 industries are included in the table. The total amount is 52, however for graphical purposes these are not included. All industry coefficients were insignificant. In future tables, this method will also be applied, making the tables more pleasant to interpret.
4.2 Analysing hypothesis 2
The second hypothesis in this study states that when applying a longer event window, the
hotmarketdummy coefficient will not have a significant effect on the CAR, compared to other market
periods, since in the long run, the significant effect of table 1 will evaporate. When looking at table 2, the coefficient of the hotmarketdummy did indeed drop with almost five percent. However, more importantly, the results are not significant. Therefore, the CAR in times of a hot market is not significantly higher than other market periods, when extending the event window. Therefore,
hypothesis 2 must be accepted. When looking at the research regarding the importance of selecting a timeframe, studies show that a long event window like -10 to 10 could lead to insignificant results, because factors other than the M&A deal could play a role in the change in stock prices for the acquiring company. Again, just as in table 1, all the control variables remain insignificant. The r-squared for table 2 is 0.564, which is slightly lower than in table 1. However, a r-r-squared of 0.564 still explains a solid amount of the entire regression.
Table 2: OLS-regression of the full sample at en event window of -10 to 10.
(1)
VARIABLES
OLS
hotmarketdummy
2.712
(5.838)
ln_DealSize
-0.495
(0.849)
ln_FirmSize
0.777
(1.050)
attitude
11.000
(7.061)
i1
6.582
(14.758)
i2
-17.522
(18.287)
i3
17.933
(16.478)
i4
-2.623
(18.224)
i5
-5.914
(18.196)
i6
10.672
(19.309)
i7
12.574
(16.004)
i8
-2.461
(15.820)
i9
-16.805
(18.203)
i10
0.807
(16.967)
Constant
-8.666
(15.796)
Observations
99
R-squared
0.564
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
OLS
4.3 Robustness check
The chosen method to define a hot market period by Tebourbi (2012) is rather restrictive. This is because only a hot market occurs when the EuroStoxx 50 was situated in the upper quartile of the timeframe and this had to hold for at least three consecutive months. This means that the hot markets in the sample are scarce. In this sample, periods of April 2003 to July 2003, April 2005 to July 2005, November 2005 to January 2006 and from July 2009 to August 2009 can be considered as hot markets. When combining all the hot markets, the sample consists of roughly a year of hot markets, whereas the sample itself consist of 15 years. Therefore, it is arguable that the procedure to define a hot market is too restrictive and that only the most certain hot market periods are included. To check if hypothesis 1 still holds in other conditions, a robustness check will be performed. The procedure of defining a hot market is eased down. Whereas the original hotmarketdummy variable only included the upper 25 percent of the index that remained so for three consecutive months, the
hotmarketdummy variable will contain the same procedure but with ‘only’ two consecutive months
required. Now, together with the already obtained hot market periods, the following periods will be added to the hotmarketdummy variable: October 2002 to November 2002, December 2003 to January 2004, April 2007 to May 2007, March 2009 to April 2009, September 2010 to October 2010, December 2010 to January 2011, January 2012 to February 2012, September 2013 to October 2013 to January 2015 to February 2015. Table 3 shows that the hotmarketdummy variable keeps its significance, however it dropped from a 5% to a 10% significance level. The r-squared of table 3 dropped with 0.002, meaning that there is basically no difference with regards to the r-squared, which make sense since all variables remain the same, only the hotmarketdummy variable is slightly
altered. Also, the coefficient did drop compared to table 1 from 7.579 to 4.751, meaning that the effect of the hotmarketdummy on the CAR dropped with 2.828. However, the results are still significant, so easing the restriction to become a hot market did not change the outcome of this study.
Table 3: OLS-regression of the full sample but with altered hotmarketdummy variable to
check for robustness.
(1)
VARIABLES
OLS
hotmarketdummy
4.751*
(2.463)
ln_DealSize
-0.089
(0.358)
ln_FirmSize
-0.026
(0.441)
attitude
3.336
(3.529)
i1
1.986
(7.282)
i2
1.806
(9.132)
i3
3.844
(8.282)
i4
1.967
(9.090)
i5
4.078
(9.048)
i6
-3.383
(9.441)
i7
0.265
(7.915)
i8
-1.632
(7.922)
i9
3.548
(9.116)
i10
3.287
(7.986)
Constant
-1.741
(8.503)
Observations
99
R-squared
0.573
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
4.4 Comparing results with prior studies
The results of hypothesis 1 are in line with most literature discussed in chapter 2. To recall, the results are significant and hypothesis has been accepted, meaning that in the short run, stock-based M&A deals during times of a hot market perform better than similar deals not performed in a hot market period. This is in line with Song (2007), Fu, Lin and Officer (2013), Savor and Lu (2009), Akbulut (2012), Fung, Jo and Tsai (2009). Therefore, this study is in line with the literature, providing the argument that the European firms react the same in the short run, compared to US firms. Therefore, this study provides new information for publicly traded European firms, because it has shown that during a hot market, the CARs of these companies did significantly outperform stock-based M&A deals in other times. This could mean that the corporate strategy of the European firms might change, due to the evidence in this paper. However, the coefficient of this study is 7.579, whereas the literature mentioned above score at least 20% higher than other market periods, ranging up to 30%. This means that the effect found in this study for European firms is present, however the effect of the
hotmarketdummy is not as strong as the US-based studies. This lower coefficient could also emerge
due to a different approach in capturing overvalued firms. This study, as stated previously, used the approach of Tebourbi (2012), whereas other studies like Song (2007), Fu, Lin and Officer (2013), Savor and Lu (2009), Akbulut (2012) used accounting financials to capture the degree of overvaluation for the acquiring companies. This approach could therefore capture a bigger effect on the CAR. However, this must be tested for European firms first, to tell whether this approach indeed leads to a higher coefficient. The arguments being made that in the long run, and even at an event window of -10 to 10, a reverse effect should occur is not supported in this study. Since only a -10 to 10 window could be used to capture this effect, because lengthening this window harms the reliability of this study, as discussed earlier. Other studies might explain some long-term effects of the stock-based M&A deals during hot market, but these studies used different approaches compared to this study, making it impossible to capture the same effect in this study with the precision of their models. However, according to table 2, the coefficient of the hotmarketdummy did indeed already drop from of 7.579 to 2.712. However, these results are insignificant and therefore a real argument that a reverse effect for the long run will occur is not yet present in this study.
5. Conclusion
The neoclassical view on M&A deals was always positive, since M&A deals created synergies and these synergies were beneficial for the company, regardless of its payment type and its market timing. However, recent empirical literature has shown that the payment type and market timing are of big importance for the success rate of the M&A deal. This study has examined the effect whether stock-based M&A deals in hot market periods performed better than stock-based deals in other periods for European firms. The dataset started in 2000 and ended in 2015. To capture the
performance prior, during and after the deal, this study has used the cumulative abnormal returns to capture the performance of the acquiring firm. To operationalize a hot market, this study used the method of Tebourbi (2012). Prior research, based on the United States, showed us that in the short run, stock-based M&A deals in hot market periods did indeed outperform the other periods. However, in the long run, the consensus was that the reverse effect happens in the long run. The results of this study, for European firms, show the same results that were previously seen for US firms, regarding the effects for the short run. Therefore hypothesis 1 must be accepted. For the long run, this study is in line with prior research, since prior research states that the CAR of the hot market period would lose its significance, compared to other markets. However, it was not in line with the studies that stated, that the CAR would be negative for the M&A deals during a hot market. The empirical evidence that has been found in this study could translate into practical relevance for European firms. As of today, we experience a hot market period, causing many publicly traded European companies to have overvalued shares. If these companies already considered the possibility for a stock-based M&A deal, now is the time to consider it, based on the short-term effects.
The major limitation of this study was the sample size. Since most of the sample was based upon the financial industry, most of the sample had to be omitted since the inclusion of the financial industry in this study would have caused bias in the results. Therefore, the sample size was rather small, making generalizability claims harder to make. However, even though the small sample size, the regressions in table 1 and 3 were significant on a respective two percent and five percent significance interval.
Future research might therefore enlarge the sample size for the European dataset by possibly prolonging the time frame of the sample, but also to make changes in the restrictions this study incorporated. Future research could, for example, decide to include from a 50% share buy-in up to the full 100% buy with shares, that this study solely uses. This, however, might alter the effect of the
hotmarketdummy variable on the CAR. Also, another proxy for the hot market period can be used.
hot market and or overvalued firms. The same could be done for European firms, to check whether the results remain the same or whether this would differ European M&A deals from the USA. At last, a comparison between the USA and Europe can be made with the method used in this study. It might be insightful to examine the effects for both areas, and to see whether one area has a
stronger/weaker effect than the other area. This could of course also be done for other areas in the world.
To conclude, this study has contributed towards the academic literature of corporate finance, due to its findings for the effect stock-based M&A deals during hot market periods in Europe
6. Bibliography
Akbulut, M. (2012). Do overvaluation-driven stock acquisitions really benefit acquirer shareholders? Journal of Financial and Quantitative Analysis, forthcoming.
Amewu, G. (2014). Implication of mergers and acquisitions on stock returns before and during the 2007-2009 credit crunch: An event study. African Review of Economics and Finance, 6(1), 102-119. Ang, J., Cheng, Y. (2006). Direct evidence on the market-driven acquisition theory. Journal of Financial Research 29, 199-216.
Berry, W. D., & Feldman, S. (1985). Quantitative Applications in the Social Sciences: Multiple regression in practice. Thousand Oaks, CA: SAGE Publications, Inc.
Brown, S. J., & Warner, J. B. (1985). Using daily stock returns: The case of event studies. Journal of Financial Economics, 14(1), 3-31.
Burgstahler, D., & Dichev, I. (1997). Earnings management to avoid earnings decreases and losses. Journal of accounting and economics, 24(1), 99-126.
Capron, L., & Pistre, N. (2002). When do acquirers earn abnormal returns? Strategic management
journal, 23(9), 781-794.
Chakraborty, S., & Ray, T. (2006). Bank-based versus market-based financial systems: A growth-theoretic analysis. Journal of Monetary Economics, 53(2), 329-350.
Cowan, A. R. (1993). Tests for cumulative abnormal returns over long periods: Simulation evidence. International Review of Financial Analysis, 2(1), 51-68.
De Bodt, E., Cousin, J. G., & Roll, R. (2015). The full stock payment marginalization in M&A transactions. Available at SSRN 2689844.
Dong, M., Hirshleifer, D., Richardson, S., Teoh, S., 2006. Does investor misvaluation drive the takeover market? Journal of Finance 61, 725-762.
Fu, F., Lin, L., & Officer, M. S. (2013). Acquisitions driven by stock overvaluation: Are they good deals?.Journal of Financial Economics, 109(1), 24-39.
Fung, S., Jo, H., & Tsai, S. C. (2009). Agency problems in stock market driven acquisitions.‐ Review of
Accounting and Finance.
Gu, F., Lev, B. (2011). Overpriced shares, ill-advised acquisitions, and goodwill impairment. Accounting Review 86, 1995-2022.
Gugler, K., Mueller, D. C., Weichselbaumer, M., & Burcin Yurtoglu, B. (2012). Market optimism and merger waves. Managerial and Decision Economics, 33(3), 159-175.
Gugler, K. P., Mueller, D. C., & Yurtoglu, B. B. (2006). The determinants of merger waves.
WZB-Markets and Politics Working Paper No. SP II, 1.
Halunga, A. G., Orme, C. D., & Yamagata, T. (2011). A heteroskedasticity robust Breusch-Pagan test for contemporaneous correlation in dynamic panel data models.
Ikram, F., & Nugroho, A. B. (2014). Cumulative average abnormal return and semistrong form efficiency testing in indonesian equity market over restructuring issue. International Journal of
Management and Sustainability, 3(9), 552-566.
Imaa-Institute, (2020). Number & Value ofM&A Worldwide. Retrieved from https://imaa-institute.org/mergers-and-acquisitions-statistics/
Jensen, M. C. (2004). The agency cost of overvalued equity and the current state of corporate finance. European Financial Management, 10(4), 549-565.
Kianifard, F., & Swallow, W. H. (1989). Using recursive residuals, calculated on adaptively-ordered observations, to identify outliers in linear regression. Biometrics, 571-585.
Lambrecht, B. M. (2004). The timing and terms of mergers motivated by economies of scale. Journal
of Financial Economics, 72(1), 41-62.
Manzoor, H. (2015). Impact of Dividends Announcements on Stock Returns Evidence from Karachi Stock Market. American Research Journal of Business and Management, 1(2), 25-36.
Morellec, E. and Zhdanov, A. (2005), “The dynamics of mergers and acquisitions”, Journal of Financial Economics, Vol. 77 No. 3, pp. 649-72.
Rhodes-Kropf, M., Viswanathan, S. (2004). Market valuation and merger waves. Journal of Finance 59, 2685-2718.
Rhodes-Kropf, M., Robinson, D., Viswanathan, S. (2005). Valuation waves and merger activity: the empirical evidence. Journal of Financial Economics 77, 561-603.
Savor, P. G., & Lu, Q. (2009). Do stock mergers create value for acquirers?. The Journal of
Finance, 64(3), 1061-1097.
Shleifer, Andrei and Robert W. Vishny. (2003). Stock Market Driven Acquisitions, Journal of Financial Economics, 70 (3), 295-489.
Shoven, J. B., Bulow, J. I., Fellner, W. J., & Gramlich, E. M. (1975). Inflation accounting and nonfinancial corporate profits: Physical assets. Brookings Papers on Economic Activity, 1975(3), 557-611.
Song, W. (2007, May). Does overvaluation lead to bad mergers?. In AFA 2007 Chicago Meetings
Paper.
Tebourbi, I. (2012). Timing of mergers and acquisitions: Evidence from the Canadian stock market. International Journal of Economics and Finance, 4(9), 87-107.
Town, R. J. (1992). Merger waves and the structure of merger and acquisition time series.‐ Journal of
applied econometrics, 7(S1), S83-S100.
7. Appendices
Mean VIF 3.21 i41 2.00 0.499867 i36 2.14 0.467761 i47 2.14 0.466727 i5 2.15 0.464753 i26 2.15 0.464433 i23 2.17 0.460602 i4 2.17 0.460462 i40 2.18 0.458393 i9 2.18 0.457802 i18 2.19 0.456713 i2 2.19 0.456231 i34 2.20 0.455274 i12 2.20 0.455177 i43 2.21 0.451488 i46 2.23 0.448061 i31 2.25 0.443884 i24 2.27 0.440038 i29 2.28 0.438800 i42 2.31 0.433309 i20 2.31 0.432706 i35 2.31 0.432454 i25 2.32 0.430741 i6 2.34 0.426842 i37 2.36 0.424580 hotmarketd~y 2.37 0.421449 attitude 2.43 0.411231 ln_DealSize 2.49 0.401947 i33 2.50 0.400471 i50 2.57 0.388856 ln_FirmSize 2.64 0.379284 i11 2.65 0.376881 i17 3.02 0.330656 i27 3.26 0.307069 i7 3.26 0.306753 i8 3.27 0.306241 i19 3.29 0.304388 i48 3.32 0.301472 i10 3.32 0.301379 i32 3.33 0.300020 i45 3.34 0.299839 i30 3.40 0.293851 i38 3.42 0.292746 i28 3.55 0.281597 i3 3.57 0.280226 i49 3.61 0.276660 i16 3.62 0.276395 i21 3.77 0.265549 i1 4.10 0.244125 i15 4.10 0.243741 i51 4.38 0.228463 i39 4.54 0.220335 i14 5.34 0.187168 i13 8.64 0.115792 i22 9.15 0.109314 i44 11.02 0.090778 Variable VIF 1/VIF99% 1 1 Kurtosis 10.46291 95% 1 1 Skewness 3.076185 90% 0 1 Variance .0750361 75% 0 1 Largest Std. Dev. .2739271 50% 0 Mean .0808081 25% 0 0 Sum of Wgt. 99 10% 0 0 Obs 99 5% 0 0 1% 0 0 Percentiles Smallest Attitude no observations Industry 99% 102717.5 146501 Kurtosis 42.91616 95% 30251.9 58934 Skewness 5.655269 90% 20920.5 49420.05 Variance 3.00e+08 75% 8805.775 31707 Largest Std. Dev. 17328.81 50% 2516.75 Mean 7969.47 25% 363.44 20.34 Sum of Wgt. 100 10% 63.69 17.55 Obs 100 5% 22.93 8.83 1% 5.175 1.52 Percentiles Smallest FirmSize 99% 83069.6 87094.73 Kurtosis 27.42849 95% 16990.67 79044.47 Skewness 4.956391 90% 9059.875 73167.04 Variance 1.93e+08 75% 2546.415 20878.34 Largest Std. Dev. 13877.82 50% 584.035 Mean 4516.589 25% 48.17 1.28 Sum of Wgt. 100 10% 6.87 1.19 Obs 100 5% 2.295 1.16 1% .775 .39 Percentiles Smallest DealSize 99% 1 1 Kurtosis 3.775068 95% 1 1 Skewness 1.665853 90% 1 1 Variance .1490909 75% 0 1 Largest Std. Dev. .3861229 50% 0 Mean .18 25% 0 0 Sum of Wgt. 100 10% 0 0 Obs 100 5% 0 0 1% 0 0 Percentiles Smallest hotmarketdummy 99% 17.44931 20.48346 Kurtosis 9.905223 95% 9.52772 14.41516 Skewness -1.151003 90% 6.690974 13.16821 Variance 38.32769 75% 3.469216 11.19103 Largest Std. Dev. 6.190936 50% 1.354257 Mean .6875632 25% -1.886085 -10.62572 Sum of Wgt. 100 10% -6.09022 -12.69849 Obs 100 5% -8.535472 -15.23118 1% -23.18297 -31.13477 Percentiles Smallest CAR
i6 -0.0145 -0.0102 -0.0102 1.0000 i5 -0.0145 -0.0102 1.0000 i4 -0.0145 1.0000 i3 1.0000 i3 i4 i5 i6 i6 -0.0190 0.3194 -0.0875 -0.1900 -0.0300 -0.0179 -0.0102 i5 0.0191 -0.0319 0.0020 0.0778 -0.0300 -0.0179 -0.0102 i4 -0.0189 -0.0319 0.1098 0.0171 -0.0300 -0.0179 -0.0102 i3 0.0247 -0.0454 -0.0081 -0.1551 -0.0426 -0.0254 -0.0145 i2 -0.0196 -0.0319 0.0696 -0.0187 -0.0300 -0.0179 1.0000 i1 0.0622 0.3540 0.0925 -0.0240 -0.0524 1.0000 attitude 0.0326 0.0352 0.0456 0.2076 1.0000 ln_FirmSize -0.1196 0.0477 0.1985 1.0000 ln_DealSize -0.0365 0.0964 1.0000 hotmarketd~y 0.1476 1.0000 CAR 1.0000 CAR hotmar~y ln_Dea~e ln_Fir~e attitude i1 i2
